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Smoothing Spline Technique For Time Series Data with Autocorrelation (in English)
Samuel Olorunfemi Adams
(Author)
·
LAP Lambert Academic Publishing
· Paperback
Smoothing Spline Technique For Time Series Data with Autocorrelation (in English) - Adams, Samuel Olorunfemi
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Synopsis "Smoothing Spline Technique For Time Series Data with Autocorrelation (in English)"
The study proposes a smoothing method which is the arithmetic weighted value of Generalized Cross-Validation (GCV) and Unbiased Risk (UBR) methods. This study concluded that the PSM method provides the best-fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5 and 0.8), and does not overfit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to: non - parametric regression, non - parametric forecasting, spatial, survival and econometrics observations.
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The book is written in English.
The binding of this edition is Paperback.
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